arXiv:2602.13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings. However, most existing methods frame anomaly detection as a purely discriminative prediction task with fixed feature representations, rather than an evidence-driven diagnostic process. As a result, they often struggle when anomalies exhibit strong context dependence, diverse patterns, or domain shifts across datasets. To address these challenges, we pr
The rapid advancement in large language models and agentic AI systems is leading to their application in increasingly complex and critical domains, including time series analysis where traditional methods fall short.
Agentic time series anomaly detection could significantly improve reliability and automation in critical infrastructure monitoring, financial fraud detection, and industrial operations.
Anomaly detection shifts from static, predictive models to dynamic, evidence-driven diagnostic processes, enabling better handling of context, diverse patterns, and domain shifts.
- · AI software developers
- · Companies with complex real-time data needs
- · Critical infrastructure operators
- · Cybersecurity firms
- · Providers of traditional anomaly detection software
- · Analysts reliant on fixed-feature models
Improved detection and quicker response to operational anomalies become possible across various industries.
Increased trust in AI's diagnostic capabilities leads to greater automation in decision-making processes.
Reduced human oversight requirements in monitoring systems, potentially leading to workforce reallocations or reductions in certain analytical roles.
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